The neural computation community has shown great interest in multivariate regression methods which can provide a clear view of the nature of relationships, using both linear and nonlinear effects. [1]} developed a fully Bayes inference method for NB responses which yields shrinkage for all of the hyperparameters. However, applying fully Bayes approaches to hierarchical models is computationally intensive. As an alternative, empirical Bayes can provide a bias-variance trade-off which can achieve a small mean square error at a lower computational cost. To estimate the unknown parameters of the model, [2]} has successfully used maximum likelihood estimation, but when the data length is small, the estimation becomes biased. The SODS estimator developed here, to find the latent interactions between neural populations, combines both of the above methods. It has the benefit of providing a shrinkage estimator for NB responses, whilst not needing the intensive computation of fully Bayes inference.
